标签团队方法：利用CLS与语言标记增强多语言ASR
The Tag-Team Approach: Leveraging CLS and Language Tagging for Enhancing Multilingual ASR
AI总结：
本文针对印度多语言ASR中文字差异大、语音数据有限的问题，提出在CLS多语言模型上注入语言ID或CLS到本地文字转换器的方法，显著降低了词错误率并验证了分布外鲁棒性。
摘要：
在像印度这样语言多样的国家构建多语言自动语音识别（ASR）系统可能是一项具有挑战性的任务，原因在于文字系统的差异以及语音数据的有限可用性。这一问题可以通过利用许多这些语言在语音上相似这一事实来解决。这些语言可以通过将相似的声音映射到共同标签，从而转换为一个共同标签集（CLS）。在本文中，探索并比较了新的方法，以提升基于CLS的多语言ASR模型的性能。通过在CLS多语言模型之上提供语言ID或使用CLS到本地文字转换器，将特定的语言信息注入ASR模型中。与CLS基线相比，这些方法在词错误率（WER）上取得了显著改善。这些方法进一步在分布外数据上进行了测试，以检验其鲁棒性。
英文摘要：
Building a multilingual Automated Speech Recognition (ASR) system in a linguistically diverse country like India can be a challenging task due to the differences in scripts and the limited availability of speech data. This problem can be solved by exploiting the fact that many of these languages are phonetically similar. These languages can be converted into a Common Label Set (CLS) by mapping similar sounds to common labels. In this paper, new approaches are explored and compared to improve the performance of CLS based multilingual ASR model. Specific language information is infused in the ASR model by giving Language ID or using CLS to Native script converter on top of the CLS Multilingual model. These methods give a significant improvement in Word Error Rate (WER) compared to the CLS baseline. These methods are further tried on out-of-distribution data to check their robustness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
